Apply GRPO/RL training with TRL to optimize language models.
npx skills add https://github.com/ovachiever/droid-tings --skill grpo-rl-training
Please help me install this Agent Skill. Skill: grpo-rl-training Source: https://github.com/ovachiever/droid-tings/tree/main/skills/grpo-rl-training Command: npx skills add https://github.com/ovachiever/droid-tings --skill grpo-rl-training
Expert patterns and templates for Group Relative Policy Optimization (GRPO) training using TRL, enabling robust reward design and production-ready workflows.
Use the production-ready template at templates/basic_grpo_training.py and customize rewards for your task.
High-intent search queries and answers about installing and using this skill.
GRPO training uses Group Relative Policy Optimization with TRL to fine-tune language models by comparing model outputs within groups and optimizing against reward signals. The Skill provides production-ready templates and reward libraries to set up end-to-end workflows from dataset preparation through evaluation and deployment.
Reward functions in GRPO can target format correctness, style adherence, or multi-objective goals depending on your task. This Skill includes a library of reward shapes for common scenarios like math reasoning, code generation, fact-checking, Q&A, and formatting control, with templates for composing multiple rewards together.
Yes. GRPO training with TRL and PEFT supports LoRA parameterization, reducing memory and compute requirements during fine-tuning. This Skill includes production-grade templates that integrate LoRA configuration directly into the training workflow.
Dataset preparation for GRPO involves structuring ground-truth data and prompt templates to support within-group comparisons and reward evaluation. This Skill covers dataset formatting, integration with the transformers and datasets libraries, and validation steps before launching training.
The pipeline spans dataset preparation, reward function design, training configuration with TRL, evaluation metrics, and deployment. This Skill provides end-to-end guidance and ready-to-use templates addressing each stage, enabling rapid prototyping across multiple task scenarios.
GRPO requires careful reward design and within-group comparison structure; misaligned rewards or small group sizes can degrade performance. This Skill documents these constraints and provides best practices for reward composition and training configuration to mitigate common pitfalls.
ovachieverTrain large language models with RLHF using Ray and vLLM acceleration.
stephanjFine-tune Large Language Models with LoRA and QLoRA techniques.
JeffallanAutomate fine-tuning workflows for large language models using LoRA/QLoRA and PEFT methods.
huggingfaceTrain and fine-tune language models on Hugging Face Jobs using TRL methods.
